Welcome to cuML’s documentation!#
cuML is a suite of fast, GPU-accelerated machine learning algorithms
designed for data science and analytical tasks. Our API mirrors scikit-learn,
providing practitioners with the familiar fit-predict-transform paradigm
without requiring GPU programming expertise. With cuml.accel
, cuML can also
automatically accelerate existing code with zero code changes.
cuML delivers on average 10-50x faster performance than CPU-based alternatives for realistic workloads and supports 50+ algorithms across all major machine learning categories, including clustering, regression, classification, dimensionality reduction, and time series analysis. With comprehensive multi-GPU and multi-node support via Dask, cuML scales from single workstations to large clusters.
Especially if your scikit-learn, umap-learn, or hdbscan workflows take many minutes to complete, you will likely benefit from using cuML. The equivalent cuML estimators often run in seconds.
Quick Start#
from cuml.datasets import make_blobs
from cuml.cluster import DBSCAN
# Create sample data
X, y = make_blobs(n_samples=100, centers=3, n_features=2, random_state=42)
# Fit clustering model
dbscan = DBSCAN(eps=1.0, min_samples=5)
dbscan.fit(X)
print(dbscan.labels_)
Key Features#
GPU Acceleration: 10-50x faster than CPU-based alternatives
Scikit-learn Compatible: Drop-in replacement for most sklearn algorithms
Multi-GPU Support: Scale across multiple GPUs and nodes with Dask
Comprehensive Coverage: 50+ algorithms across all major ML categories
Flexible Input: Works with NumPy, cuDF, cuPy, and PyTorch tensors
Production Ready: Battle-tested in enterprise environments
Installation#
cuML is available through conda and pip. For detailed installation instructions, visit the RAPIDS Release Selector.
Note
cuML is only supported on Linux operating systems and WSL 2. See the RAPIDS install page for details on system and hardware requirements.
Part of RAPIDS#
cuML is part of the RAPIDS suite of open source libraries that enable end-to-end data science and analytics pipelines entirely on GPUs. It works seamlessly with other RAPIDS libraries like cuDF for data manipulation and cuGraph for graph analytics.
Community & Support#
User Guide - Comprehensive usage documentation
API Reference - Complete API documentation
GitHub Issues - Report bugs and request features
RAPIDS Community - Join our community